Extensions

Sentiment Analysis Layer

Your product notices when a customer is frustrated or delighted, and alerts the right person while it still matters.

An upset customer is easiest to win back while the conversation is still fresh. This add-on reads each conversation in the product you already run for how the customer feels. Strong frustration or delight triggers an alert to the person you choose, with the conversation attached. You set the thresholds and the recipients, and sentiment is recorded on every conversation.

Alerts a named person when a customer is clearly upset
Spots delighted customers worth asking for a review
Records sentiment on every conversation for later review

$1,500 10–20 working days

Choose delivery and add to cart Ask an expert

Unmarked brass dial with its needle tilted beside a jar of pebbles, customer mood read in each conversation.

What it is

The Sentiment Analysis Layer adds sentiment detection and alerts to a conversational AI product you already run, or are buying now. Each conversation is read for how the customer feels, and strong negative or positive signals trigger an alert to the person you choose. It is tested on sample conversations first.

Why businesses buy it

Frustrated customers rarely complain directly. They leave, post a review or tell a friend. By the time anyone notices, the chance to fix it has gone. Sentiment alerts give your team a timely nudge to step in, and they also highlight happy customers who are worth asking for a testimonial. The written scope covers the signals, thresholds, recipients and tests on sample conversations, so alerts are useful rather than noisy from the first day.

Problems this solves

An upset customer finishes a chat and nobody on the team knows

The Sentiment Analysis Layer reads each conversation for how the customer feels. Strong negative signals trigger an alert to the person you choose, so they can follow up.

Happy customers go unnoticed when they could leave a review

Strong positive signals can trigger an alert to the person you choose as well. Your team can then thank the customer or ask for a review while the experience is fresh.

Managers lack a feel for customer mood across many conversations

Sentiment is detected on every conversation, not just a sample. It is tested on sample conversations first, and the test report shows how it read them.

Scope card SKU UPM-A0024

Sentiment Analysis Layer

Everything this build includes, written down before you pay. Choose how you want it, own the code or license it yearly, then the delivery speed and any add-ons.

$1,500 one-time build price

What is included

Channel: As per the parent product
Integrations: As described in the add-on
Knowledge: As per the parent product
Workflows: Adds sentiment detection and alerts to an existing product
Languages: As per the parent product
Testing: Tested together with the parent product
Revisions: 1 round
Handoff: Written handover report
Support: 14 days after launch

A good fit if

Upset customers reach you by complaint or a bad review before anyone noticed in the conversation.

You want a person alerted while a frustrated customer is still reachable.

You would like to spot happy customers too, for reviews or referrals.

Your product is a voice, messaging, chat or action agent.

Not the right fit if

You want themes from surveys, tickets and calls grouped into insights. AI Customer Feedback Intelligence does that.

You want to track what people say about your brand on social platforms. AI Social Listening monitors it.

You want conversations scored on accuracy and rule-following. Conversation QA Scoring scores quality.

You want frustrated customers passed straight to a person mid-conversation. Human Handoff Setup adds live handoff.

How it works

Signals are agreed. What counts as strongly negative or strongly positive for your customers.
Each conversation is read for sentiment, during or at the end, depending on the parent product.
Alerts go to the right person, with the conversation and the reason, through email, Slack or Teams.
Sentiment is recorded against each conversation, so trends can be seen over time.
Detection is tested with the parent product on sample conversations, then reviewed by you with one revision round.

What you provide within 72 hours

Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.

The order reference of the product getting sentiment detection
What should trigger an alert, and who receives it
Where alerts should arrive: email, Slack or Teams
Example conversations you consider negative or positive, if you have them
One person who owns the alert rules

The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.

Timeline

Start Alert rules and recipients agreed

Week 1 Sentiment signals defined with examples

Week 2 Detection and alerts built

Week 2 Sample conversations tested for sentiment

Week 2 Your review and one revision round

Go-live Sentiment layer live, alert rules documented

Delivery options, in working days (Monday to Friday) from completed intake. They can extend because of national or public holidays, technical issues outside our control (such as platform outages, third-party changes, account approvals or access delays), or natural disasters and other events beyond our control. If a delay happens, we tell you in writing with a new date.
Option	Delivery time	Price
Standard	10–20 working days	$1,500

What you receive at handover

Sentiment detection added to your existing conversational product
Alerts for strong negative and positive signals, sent to the people you chose
A test report from sample conversations
Updated documentation, a handoff note and a short written walkthrough

Expected benefits and how to measure them

Can help your team reach unhappy customers sooner
Makes it easier to spot moments worth a review request
Gives managers a clearer sense of customer mood

Negative alerts followed up: Log each negative alert and whether someone contacted the customer, and review the share monthly.
Follow-up time: Measure the gap between an alert and the first follow-up, using alert and contact timestamps.
Reviews from positive alerts: Count review requests sent after positive alerts and how many led to a review over each month.

Before you start: Note how your team finds out about unhappy customers today and how long that takes. Record your current monthly review count.

These are things to track, not promised results. Outcomes depend on your data, your team and your market.

Running costs

Reading each conversation for sentiment uses some AI model usage, billed to your own model accounts. Alerts travel through email or chat tools you already pay for. We charge once for the layer.

Ask about running costs

Custom requirements

Sentiment trend reports, sentiment from sources outside the product such as reviews, or different alert rules per team are beyond this add-on. Add a note at checkout for a written Change Quote. Larger or bespoke projects go through the Custom AI request form instead.

Request a custom version

Keep it running

The layer is covered by the parent product's maintenance, which grows in line with the add-on's price. Changing alert thresholds or recipients uses its monthly change hours.

Adds $75/mo to maintenance

Covered by the main product's maintenance, which grows by this amount.

How maintenance works

How this compares

	Do it yourself with a SaaS tool	Hire a custom agency	Upmark Labs
Price	A sentiment feature bundled in a costly analytics plan	An agency quotes custom analysis	Ours is one fixed price, shown before you buy
Alerts	A score on a dashboard you check later	Depends on the brief	Ours alerts a named person with the conversation
Rules	Generic thresholds	Agreed in the project	Ours uses signals agreed for your customers
Testing	Left to you	Set per project	Ours is tested on sample conversations

Questions about this product

How does the layer decide a customer is upset?

It reads the conversation for signals agreed at intake, such as repeated complaints, strong language, threats to cancel or asking for a manager. Example conversations you supply help set those signals. The tests use sample conversations so you can see what triggers an alert before launch.

Who receives the sentiment alerts?

The people you choose, through email, Slack or Microsoft Teams. Each alert includes the conversation and the reason it was flagged, so the person can act quickly. Different signals can go to different people, for example complaints to support and praise to marketing.

Can it spot happy customers as well?

Yes. Strongly positive conversations can trigger alerts too, which is useful if you ask satisfied customers for reviews or referrals. You decide at intake whether positive alerts are wanted and who should receive them. Positive alerts follow the same rules as negative ones: the conversation and the reason are attached, so the right person can follow up while the customer is still pleased.

Does the sentiment layer go live with its parent product?

Yes. It is tested together with the product it reads and follows that product's delivery speed, so both go live at the same time. Faster speeds are available, each with a fixed price and date shown before you pay. During intake, tell us who should receive alerts and how quickly they can respond.

Does the layer change what the agent says?

No. It reads conversations and raises alerts; it does not alter replies. If you want frustrated customers passed to a person during the conversation, Human Handoff Setup adds that, and the two work well together. Sentiment is read in the background, so customers do not see anything different in the conversation itself.

Is sentiment kept for later analysis?

Yes. Sentiment is recorded against each conversation, so you can see it alongside the conversation history. Charts of sentiment over time are not included, but the Analytics Dashboard Add-on can show them using the recorded values. The values sit with the conversation history the parent product already keeps, under the same access rules.